





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]=7   ,    lowerCAmelCase_					:						Optional[int]=3   ,    lowerCAmelCase_					:						List[Any]=3_0   ,    lowerCAmelCase_					:						List[str]=4_0_0   ,    lowerCAmelCase_					:						int=True   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						List[Any]=0.9   ,    lowerCAmelCase_					:						Any=None   ,    lowerCAmelCase_					:						Dict=True   ,    lowerCAmelCase_					:						Optional[Any]=[0.5, 0.5, 0.5]   ,    lowerCAmelCase_					:						List[str]=[0.5, 0.5, 0.5]   ,    )				->		List[str]:
     __lowerCAmelCase				      =      size if size is not None else {'shortest_edge': 3_0}
     __lowerCAmelCase				      =      crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      min_resolution
     __lowerCAmelCase				      =      max_resolution
     __lowerCAmelCase				      =      do_resize_and_center_crop
     __lowerCAmelCase				      =      size
     __lowerCAmelCase				      =      crop_pct
     __lowerCAmelCase				      =      crop_size
     __lowerCAmelCase				      =      do_normalize
     __lowerCAmelCase				      =      image_mean
     __lowerCAmelCase				      =      image_std






  def 							lowercase						(							self					:						str	)				->		Tuple:
     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }





@require_torch
@require_vision
class 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					PoolFormerImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						int	)				->		str:
     __lowerCAmelCase				      =      PoolFormerImageProcessingTester(self	)






  @property
  def 							lowercase						(							self					:						List[Any]	)				->		Any:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_resize_and_center_crop'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'size'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'crop_pct'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_normalize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_mean'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_std'	)	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 3_0}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 3_0, 'width': 3_0}	)

     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict   ,    size=4_2   ,    crop_size=8_4	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 4_2}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 8_4, 'width': 8_4}	)






  def 							lowercase						(							self					:						List[Any]	)				->		str:
     pass






  def 							lowercase						(							self					:						Tuple	)				->		Tuple:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values

     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_   ,    numpify=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    np.ndarray	)

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						Dict	)				->		Optional[Any]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_   ,    torchify=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    torch.Tensor	)

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )


import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image

				from transformers.image_utils import load_image
else:





				class     lowerCAmelCase__     :


						'''simple docstring'''






						@staticmethod
						def   _snake_case       (       *snake_case__     :     Optional[int]     ,  **snake_case__     :     Tuple		)      ->	Optional[Any]:
								pass



				def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Tuple:
						return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A						=			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)





@is_pipeline_test
@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
		@require_pytesseract
		@require_vision
		def   _snake_case       (       self     :     str     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model=snake_case__     ,  tokenizer=snake_case__     ,  image_processor=snake_case__		)

				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  list(zip(*apply_tesseract(load_image(snake_case__		)     ,  snake_case__     ,  ''		)		)		)
				_lowerCamelCase				   =  'What is the placebo?'
				_lowerCamelCase				   =  [
				    {
				        'image': load_image(snake_case__		),
				        'question': question,
				    },
				    {
				        'image': image,
				        'question': question,
				    },
				    {
				        'image': image,
				        'question': question,
				        'word_boxes': word_boxes,
				    },
				]
				return dqa_pipeline, examples
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     List[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  dqa_pipeline(snake_case__     ,  top_k=2		)
				self.assertEqual(
				    snake_case__     ,  [
				        [
				            {'score': ANY(snake_case__		), 'answer': ANY(snake_case__		), 'start': ANY(snake_case__		), 'end': ANY(snake_case__		)},
				            {'score': ANY(snake_case__		), 'answer': ANY(snake_case__		), 'start': ANY(snake_case__		), 'end': ANY(snake_case__		)},
				        ]
				    ]
				    * 3     ,  )
		@require_torch
		@require_detectrona
		@require_pytesseract
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				_lowerCamelCase				   =  pipeline('document-question-answering'     ,  model='hf-internal-testing/tiny-random-layoutlmv2'		)
				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'How many cats are there?'

				_lowerCamelCase				   =  [
				    {'score': 0.0001, 'answer': 'oy 2312/2019', 'start': 3_8, 'end': 3_9},
				    {'score': 0.0001, 'answer': 'oy 2312/2019 DUE', 'start': 3_8, 'end': 4_0},
				]
				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(nested_simplify(snake_case__     ,  decimals=4		)     ,  snake_case__		)

				_lowerCamelCase				   =  dqa_pipeline({'image': image, 'question': question}     ,  top_k=2		)
				self.assertEqual(nested_simplify(snake_case__     ,  decimals=4		)     ,  snake_case__		)

				# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
				# Empty answer probably
				_lowerCamelCase				   =  './tests/fixtures/tests_samples/COCO/000000039769.png'
				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(snake_case__     ,  []		)

				# We can optionnally pass directly the words and bounding boxes
				_lowerCamelCase				   =  './tests/fixtures/tests_samples/COCO/000000039769.png'
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  words=snake_case__     ,  boxes=snake_case__     ,  top_k=2		)
				self.assertEqual(snake_case__     ,  []		)
		@slow
		@require_torch
		@require_detectrona
		@require_pytesseract
		def   _snake_case       (       self     :     Optional[int]		)      ->	List[Any]:
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'     ,  revision='9977165'     ,  )
				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'What is the invoice number?'

				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline({'image': image, 'question': question}     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        [
				            {'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				            {'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        ],
				    ]
				    * 2     ,  )
		@slow
		@require_torch
		@require_detectrona
		@require_pytesseract
		def   _snake_case       (       self     :     int		)      ->	Optional[Any]:
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'     ,  revision='9977165'     ,  max_seq_len=5_0     ,  )
				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'What is the invoice number?'

				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				        {'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline({'image': image, 'question': question}     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				        {'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        [
				            {'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				            {'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        ]
				    ]
				    * 2     ,  )
		@slow
		@require_torch
		@require_pytesseract
		@require_vision
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  AutoTokenizer.from_pretrained(
				    'impira/layoutlm-document-qa'     ,  revision='3dc6de3'     ,  add_prefix_space=snake_case__		)
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model='impira/layoutlm-document-qa'     ,  tokenizer=snake_case__     ,  revision='3dc6de3'     ,  )
				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'What is the invoice number?'

				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline({'image': image, 'question': question}     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        [
				            {'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				            {'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				        ]
				    ]
				    * 2     ,  )

				_lowerCamelCase				   =  list(zip(*apply_tesseract(load_image(snake_case__		)     ,  snake_case__     ,  ''		)		)		)

				# This model should also work if `image` is set to None
				_lowerCamelCase				   =  dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
				    ]     ,  )
		@slow
		@require_torch
		@require_pytesseract
		@require_vision
		def   _snake_case       (       self     :     Dict		)      ->	List[str]:
				_lowerCamelCase				   =  AutoTokenizer.from_pretrained(
				    'impira/layoutlm-document-qa'     ,  revision='3dc6de3'     ,  add_prefix_space=snake_case__		)
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model='impira/layoutlm-document-qa'     ,  tokenizer=snake_case__     ,  revision='3dc6de3'     ,  max_seq_len=5_0     ,  )
				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'What is the invoice number?'

				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )

				_lowerCamelCase				   =  dqa_pipeline(
				    [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        [
				            {'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				            {'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        ]
				    ]
				    * 2     ,  )

				_lowerCamelCase				   =  list(zip(*apply_tesseract(load_image(snake_case__		)     ,  snake_case__     ,  ''		)		)		)

				# This model should also work if `image` is set to None
				_lowerCamelCase				   =  dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        {'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				        {'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
				    ]     ,  )
		@slow
		@require_torch
		def   _snake_case       (       self     :     Dict		)      ->	int:
				_lowerCamelCase				   =  pipeline(
				    'document-question-answering'     ,  model='naver-clova-ix/donut-base-finetuned-docvqa'     ,  tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa'		)     ,  feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa'     ,  )

				_lowerCamelCase				   =  INVOICE_URL
				_lowerCamelCase				   =  'What is the invoice number?'
				_lowerCamelCase				   =  dqa_pipeline(image=snake_case__     ,  question=snake_case__     ,  top_k=2		)
				self.assertEqual(nested_simplify(snake_case__     ,  decimals=4		)     ,  [{'answer': 'us-001'}]		)



		@require_tf
		@unittest.skip('Document question answering not implemented in TF'		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Any:
				pass